Affiliation: Center for Health Informatics and Bioinformatics, New York University School of Medicine, New York, NY 10016, USA. alexander.alekseyenko@nyumc.org

ABSTRACT

Background: GWAS owe their popularity to the expectation that they will make a major impact on diagnosis, prognosis and management of disease by uncovering genetics underlying clinical phenotypes. The dominant paradigm in GWAS data analysis so far consists of extensive reliance on methods that emphasize contribution of individual SNPs to statistical association with phenotypes. Multivariate methods, however, can extract more information by considering associations of multiple SNPs simultaneously. Recent advances in other genomics domains pinpoint multivariate causal graph-based inference as a promising principled analysis framework for high-throughput data. Designed to discover biomarkers in the local causal pathway of the phenotype, these methods lead to accurate and highly parsimonious multivariate predictive models. In this paper, we investigate the applicability of causal graph-based method TIE* to analysis of GWAS data. To test the utility of TIE*, we focus on anti-CCP positive rheumatoid arthritis (RA) GWAS datasets, where there is a general consensus in the community about the major genetic determinants of the disease.

Results: Application of TIE* to the North American Rheumatoid Arthritis Cohort (NARAC) GWAS data results in six SNPs, mostly from the MHC locus. Using these SNPs we develop two predictive models that can classify cases and disease-free controls with an accuracy of 0.81 area under the ROC curve, as verified in independent testing data from the same cohort. The predictive performance of these models generalizes reasonably well to Swedish subjects from the closely related but not identical Epidemiological Investigation of Rheumatoid Arthritis (EIRA) cohort with 0.71-0.78 area under the ROC curve. Moreover, the SNPs identified by the TIE* method render many other previously known SNP associations conditionally independent of the phenotype.

Conclusions: Our experiments demonstrate that application of TIE* captures maximum amount of genetic information about RA in the data and recapitulates the major consensus findings about the genetic factors of this disease. In addition, TIE* yields reproducible markers and signatures of RA. This suggests that principled multivariate causal and predictive framework for GWAS analysis empowers the community with a new tool for high-quality and more efficient discovery.

Mentions:
Using EIRA data we can also explore whether SNP rs12523624 (that does not belong to the MHC locus), which has been found to be statistically non-robust in NARAC and invalidated in the meta-analysis in several cohorts, is predictively essential for the causal graph-based model of rheumatoid arthritis. We build a modified model by removing SNP rs12523624 from the first Markov boundary and fitting kernel ridge regression to the remaining 4 SNPs in NARAC training data. Interestingly, the resulting model yields slightly better predictive accuracy than the original 5-SNP model (Figure 6). However, the observed differences are not statistically significant. Hence we conclude that the effect of SNP rs12523624 on predicting rheumatoid arthritis is small and this SNP is likely to be a false positive marker. However, we note that the results in Figure 6 may be slightly overoptimistic because both statistical robustness analysis and meta-analysis (that informed the removal of SNP rs12523624) utilize subjects used for validation of the model.

Mentions:
Using EIRA data we can also explore whether SNP rs12523624 (that does not belong to the MHC locus), which has been found to be statistically non-robust in NARAC and invalidated in the meta-analysis in several cohorts, is predictively essential for the causal graph-based model of rheumatoid arthritis. We build a modified model by removing SNP rs12523624 from the first Markov boundary and fitting kernel ridge regression to the remaining 4 SNPs in NARAC training data. Interestingly, the resulting model yields slightly better predictive accuracy than the original 5-SNP model (Figure 6). However, the observed differences are not statistically significant. Hence we conclude that the effect of SNP rs12523624 on predicting rheumatoid arthritis is small and this SNP is likely to be a false positive marker. However, we note that the results in Figure 6 may be slightly overoptimistic because both statistical robustness analysis and meta-analysis (that informed the removal of SNP rs12523624) utilize subjects used for validation of the model.

Affiliation:
Center for Health Informatics and Bioinformatics, New York University School of Medicine, New York, NY 10016, USA. alexander.alekseyenko@nyumc.org

ABSTRACT

Background: GWAS owe their popularity to the expectation that they will make a major impact on diagnosis, prognosis and management of disease by uncovering genetics underlying clinical phenotypes. The dominant paradigm in GWAS data analysis so far consists of extensive reliance on methods that emphasize contribution of individual SNPs to statistical association with phenotypes. Multivariate methods, however, can extract more information by considering associations of multiple SNPs simultaneously. Recent advances in other genomics domains pinpoint multivariate causal graph-based inference as a promising principled analysis framework for high-throughput data. Designed to discover biomarkers in the local causal pathway of the phenotype, these methods lead to accurate and highly parsimonious multivariate predictive models. In this paper, we investigate the applicability of causal graph-based method TIE* to analysis of GWAS data. To test the utility of TIE*, we focus on anti-CCP positive rheumatoid arthritis (RA) GWAS datasets, where there is a general consensus in the community about the major genetic determinants of the disease.

Results: Application of TIE* to the North American Rheumatoid Arthritis Cohort (NARAC) GWAS data results in six SNPs, mostly from the MHC locus. Using these SNPs we develop two predictive models that can classify cases and disease-free controls with an accuracy of 0.81 area under the ROC curve, as verified in independent testing data from the same cohort. The predictive performance of these models generalizes reasonably well to Swedish subjects from the closely related but not identical Epidemiological Investigation of Rheumatoid Arthritis (EIRA) cohort with 0.71-0.78 area under the ROC curve. Moreover, the SNPs identified by the TIE* method render many other previously known SNP associations conditionally independent of the phenotype.

Conclusions: Our experiments demonstrate that application of TIE* captures maximum amount of genetic information about RA in the data and recapitulates the major consensus findings about the genetic factors of this disease. In addition, TIE* yields reproducible markers and signatures of RA. This suggests that principled multivariate causal and predictive framework for GWAS analysis empowers the community with a new tool for high-quality and more efficient discovery.